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ATISA: Adaptive Threshold-based Instance Selection Algorithm
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DOI：10.1016/j.eswa.2013.06.053.png)
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Abstract
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Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Instance selection
Instance-based learning algorithms
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